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Anaconda Enterprise REVIEW

Predictive Analysis Software

No user reviews
USER SATISFACTION 96%
OUR SCORE 9.2

What is Anaconda Enterprise?

Anaconda Enterprise is a modern and dynamic data science software platform that allows teams of data scientists to create, supervise, and automate AI-powered data science models and pipelines across production environments and server clusters. Anaconda Enterprise is equipped with collaboration features which enable team members to share data science projects with each other and edit them in real time. They can also make their resources readily accessible to everyone in their team such as machine-learning models, online notebooks, dependencies, and dashboards.

Anaconda Enterprise is a scalable data science solution which means users will be able to quickly provision their data science projects and applications  with all the necessary computation resources they need. The platform permits them to deploy their data science models and resources to either on-premises or cloud-based environments.

With Anaconda Enterprise, the tools, data science model versions, and packages being utilized by teams can be easily managed, governed, and controlled. The platform can generate logs that record the activities being performed by each team member. For a more secure collaboration, Anaconda Enterprise implements TTL or SSL encryption so that team members can communicate and collaborate over their network with confidence, without being bothered by security breaches and issues like data leakage, data theft, and more.

Overview of Anaconda Enterprise Benefits

Enterprise-Ready Data Science Platform

Anaconda Enterprise is an enterprise-ready data science platform. But what is data science really all about? Data science is a field that applies various theories, methods, systems, and processes from different fields and disciplines in order to gain insights and knowledge from data. It combines scientific methods, mathematics, statistics, information science, and computer science; and can be applied to activities related to business intelligence, business analytics, and predictive modeling. Anaconda Enterprise automates how organizations and businesses implement data science projects, models, and processes; helping them perform data analytics much better.

Real-Time Collaboration

Collaboration is one of the key features offered by Anaconda Enterprise. It unifies data science projects and the corresponding resources in one central location and permits team members to collaborate in real time. Team members can share data science projects and dependencies with their colleagues and the latter can access all of them from a convenient place.

Access Convenient Data Science Environments Through Jupyter Integration

As part of its real-time collaboration capabilities, Anaconda Enterprise integrates with Jupyter-based software solutions which include Jupyter Notebooks and JupyterLab. Jupyter Notebooks is an open-source and open-standards web-based tool which allows users to build and share documents for data visualization, data cleaning, numerical simulation, machine learning, or statistical modeling.

These documents are called notebooks or web-based documents that contain live codes, equations, data visualizations, and narrative texts. Meanwhile, JupyterLab is a computational environment that provides an intuitive user interface for managing Jupyter projects. Because Anaconda Enterprise supports integration with these solutions, users will be able to collaborate directly on the Jupyter web-based documents they are sharing with their team members. In addition, the platform lets them handle different versions of their browser-based notebooks as well as control how those notebooks are being accessed.  

Scalable Architecture That Can Automatically Adjust

The enterprise data science platform has a scalable architecture, and that can be observed in its ability to distribute Anaconda Enterprise libraries and resources across the clusters of servers handled by Hadoop and Apache Spark. Hadoop is a big-data framework which distributes large collections of data across multiple nodes within a cluster of servers. When it comes to Apache Spark, this is also a big-data framework which is used for processing those data collections. As a scalable data science solution, Anaconda Enterprise can automatically scale up or down depending on the number of cluster nodes that users need to distribute data collections to. For example, users will be able to instantly add new cluster nodes or delete existing ones.

Self-Service Deployment Features

Anaconda Enterprise is built with amazing deployment features. For example, it has a self-service deployment capability which enables users to gain full flexibility and control over the deployment of AI-powered data science models, dashboards, and browser-based notebooks. With just a single click, users can deploy any of these. Another deployment feature available in Anaconda Enterprise is its remote deployment capability. Here, users will be able to remotely deploy their data science projects and models,  and computational resources to Hadoop or Apache Spark server clusters. Deployments can also be easily managed for on-premise environments and cloud-based services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud.

Complete Governance Of Data Science Packages, Versions, And Tools

Governing data science projects, models, and resources is made easy using Anaconda Enterprise. It has an online repository where on-premises data science packages and stacks can be stored. Furthermore, whatever tools, packages, and versions data analysts and scientists are utilizing; the platform makes sure that they are able to control them.

License Management, Auditing, And Reporting

The data science platform also has license management feature. It lets users filter licenses associated with their data science projects and applications. In addition, the platform has the ability to generate reports as they audit their licenses.

Event Logging And Tracking

Another governance feature of Anaconda Enterprise is that it enables users to record and track all activities and events related to their data science projects, packages, and deployments. Thus, these events and activities can be logged and audited effortlessly.

Token-Based Acess

Anaconda Enterprise provides security features which help teams guarantee that their projects and resources are accessed only by authorized individuals. The platform uses a token-based system of accessing the deployed data science models and applications. In other words, users will be able to utilize their models and applications without exposing any sensitive data or information. In a token-based system,  data or information are represented by tokens. Then, these tokens are the ones being stored and processed instead of the original data or information.

Centralized Management Of Access Controls

Furthermore, Anaconda Enterprise centralizes the management of access controls and configurations. Whether users are handling access on a per user, role, or group basis; they can do this in a central location. Additionally, the enterprise data science solution supports integration with identity providers like LDAP, Active Directory, SAML, and Kerberos. Thus, users can access their data science projects, models, and resources using their credentials and profiles from their existing identify provider; streamlining user authentication and access.

Overview of Anaconda Enterprise Features

  • Collaboration
  • Integrated Jupyter Data Science Environements
  • Browser-Based Notebook Collaboration
  • Versioning and Access Control
  • Manage and Share Data Science Projects and Dependencies
  • Scalable
  • Scalable Distribution of Computational Resources
  • Distribute Anaconda Libraries across Hadoop and Spark Clusters
  • Self-Service Deployment of Models, Notebooks, and Dashboards
  • One-Click Deployment
  • Remote Deployment to Hadoop or Spark Clusters
  • On-Premise And Cloud Deployments
  • Governance
  • On-Premise Data Science Package Repository
  • Control Packages, Versions, and Tools
  • License Filtering and Auditing
  • Event Logging and Tracking
  • Security
  • Integration with Identity Providers
  • TLS/SSL Encryption
  • Centralized User, Role, or Group-Based Access Control Management and Confuguration
  • Token-Based Access to Data Science Models and Applications

Anaconda Enterprise Position In Our Categories

Position of Anaconda Enterprise in our main categories:

10

Anaconda Enterprise is one of the top 10 Predictive Analysis Softwareproducts

10

Anaconda Enterprise is one of the 10
Predictive Analysis Software products


If you are considering Anaconda Enterprise it may also be sensible to investigate other subcategories of Best Predictive Analysis Software gathered in our base of SaaS software reviews.

Companies have different wants and requirements and no software platform can be ideal in such a condition. It is pointless to try to find an ideal out-of-the-box software system that fulfills all your business requirements. The smart thing to do would be to adapt the system for your unique wants, worker skill levels, budget, and other elements. For these reasons, do not rush and subscribe to well-publicized popular solutions. Though these may be widely used, they may not be the ideal fit for your particular wants. Do your research, investigate each short-listed platform in detail, read a few Anaconda Enterprise reviews, speak to the vendor for clarifications, and finally choose the application that offers what you want.

How Much Does Anaconda Enterprise Cost?

Anaconda Enterprise’s SMB and enterprise pricing plan information is available only upon request. Please contact the sales team. and get your quote.

User Satisfaction

We realize that when you make a decision to buy Predictive Analysis Software it’s important not only to see how experts evaluate it in their reviews, but also to find out if the real people and companies that buy it are actually satisfied with the product. That’s why we’ve created our behavior-based Customer Satisfaction Algorithm™ that gathers customer reviews, comments and Anaconda Enterprise reviews across a wide range of social media sites. The data is then presented in an easy to digest form showing how many people had positive and negative experience with Anaconda Enterprise. With that information at hand you should be equipped to make an informed buying decision that you won’t regret.

POSITIVE SOCIAL MENTIONS

35

NEGATIVE SOCIAL MENTIONS

1

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Technical details

Devices Supported
  • Windows
  • Linux
  • Mac
  • Web-based
Language Support
  • English
Pricing Model
  • Quote-based
Customer Types
  • Small Business
  • Large Enterprises
  • Medium Business
Deployment
  • Cloud Hosted
  • On Premise

What Support Does This Vendor Offer?

  • EMAIL
  • PHONE
  • LIVE SUPPORT
  • TRAINING
  • TICKETS

What are Anaconda Enterprise pricing details?

Anaconda Enterprise’s SMB and enterprise pricing plan information is available only upon request. Please contact the sales team. and get your quote.

What integrations are available for Anaconda Enterprise?

Anaconda Enterprise supports integration with the following big data frameworks, data management solutions, identity providers, and cloud services:

  • Jupyter Notebooks
  • JupyterLab
  • Cloudera
  • Hortonworks
  • LDAP
  • Active Directory
  • SAML
  • Kerberos
  • Amazon Web Services (AWS)
  • Microsoft Azure
  • Google Cloud

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